Online news services such as Microsoft News have gained huge popularity for online news reading. However, since massive news articles are published everyday, users of online news services are facing heavy information overload. Therefore, news recommendation is an important technique for personalized news services to improve the reading experience of users and alleviate information overload.
However, news recommendation is a challenging task. First, news articles on news websites emerge and update very quickly. Many new articles are posted continuously, and existing news articles will disappear after a short period of time. Thus, there is a severe cold-start problem in news recommendation. Second, news articles usually contain rich textual information such as title and body. It is very important to understand news content from their texts using NLP techniques. Third, there is no explicit rating of news articles posted by users in news platforms. Thus, in news recommendation we need to model users’ interests from their browsing and click behaviors. However, user interests are usually diverse and dynamic, which poses significant challenges to user modeling algorithms. Thus, further researches are highly needed to tackle the various challenges in news recommendation.
To promote the research and practice on news recommendation, we held a MIND News Recommendation Competition based on the MIND dataset from July to September, 2020. This competition provided a good testbed for participants to develop better news recommender systems to improve the future reading experience of millions of users.
Winners and Reports
Winners of the MIND News Recommendation Competition and their technical reports are displayed as follows:
Second Place Prizes
Third Place Prizes